title: Classification of vibratory patterns of the upper airway during sleep.
abstract: Upper airway (UA) narrowing and collapse during sleep results in obstructive sleep apnea (OSA). We hypothesize that vibratory patterns of snoring can distinguish simple snorers from those with OSA. Samples of breath sounds were collected from 7 snorers without OSA and 5 with OSA. Snoring pitch (F0) contours were found using the robust algorithm for pitch tracking (RAPT). The OSA snoring contours showed fluctuating patterns as compared to the smoother patterns of simple snorers. This suggests that snoring reveals the underlying instabilities of UA tissue in OSA. Conditional random fields, a statistical sequence classifier, gave 75% accuracy in distinguishing the 2 groups.
authors.forename: Hisham
authors.initials: H
authors.lastname: Alshaer
authors.forename: Frank
authors.initials: F
authors.lastname: Rudzicz
authors.forename: Tiago H
authors.initials: TH
authors.lastname: Falk
authors.forename: Wen-Hou
authors.initials: WH
authors.lastname: Tseng
authors.forename: T Douglas
authors.initials: TD
authors.lastname: Bradley
mesh_terms: D000465:Algorithms; D006801:Humans; D012119:Respiration; D012137:Respiratory System / Q000503:physiopathology*; D012890:Sleep / Q000502:physiology*; D020181:Sleep Apnea, Obstructive / Q000150:complications / Q000503:physiopathology; D012913:Snoring / Q000150:complications / Q000503:physiopathology*; D014732:Vibration